Generative AI and Agentic AI Systems and Methods for Prevention, Detection, Mitigation and Remediation of Cybersecurity Threats

1. A generative artificial intelligence (AI) system for cybersecurity applications, comprising: a plurality of data sources, wherein at least one of the data sources is sensor data derived from direct observation of activity within an environment; a context-aware AI database; a probationary database; an analytics engine communicatively coupled to the plurality of data sources, the context-aware AI database, and the probationary database; wherein the analytics engine is configured to: (a) generate a hypothesis object comprising independent variables, a dependent variable including a leading indicator of cybersecurity attack activity, a machine learning model trained from available data, and metadata associated therewith based on the data sources, wherein the leading indicator includes a recommendation or action related to at least one of: prevention of a cyberattack, detection of a cyberattack, mitigation of a cyberattack, and remediation of a cyberattack; (b) train the machine learning model associated with the hypothesis object to produce experimental results; (c) store the hypothesis object and the experimental results in the context-aware AI database in response to determining that the performance metric of the machine learning model meets a predetermined performance criterion; (d) store the hypothesis object and the experimental results in the probationary database in response to determining that the performance metric of the machine learning model does not meet a predetermined performance criterion; and a publishing module configured to provide, to one or more subscribers, the leading indicator associated with at least one of the corporate entity and a product associated with the corporate entity, as computed by the trained machine learning model stored within the context-aware AI database while processing contemporaneous information received from the data sources.

2. The generative AI system of claim 1, wherein the analytics engine is communicatively coupled via a network to a plurality of external sources, the external sources configured to store information regarding third-party hypotheses and associated experimental results for use by the analytics engine in generating the hypothesis object.

3. The generative AI system of claim 2, wherein the analytics engine includes an AI agent configured to perform a series of actions autonomously, the actions including at least generating the hypothesis object, training the machine learning model, interrogating the context-aware AI database, interrogating the probationary database, and interrogating the plurality of external sources, executing on either alerts (HITM) or actions that are carried out autonomously within defined parameter windows.

4. The generative AI system of claim 1, wherein the analytics engine includes a natural language processing (NLP) module.

5. The generative AI system of claim 4, wherein the NLP module includes at least one transformer-based large language model (LLM).

6. The generative AI system of claim 1, wherein the analytics engine includes a large language model (LLM).

7. The generative AI system of claim 6, wherein the LLM performs instantaneous analysis of publicly or privately available cybersecurity information which can include wired and wireless network activity analysis, evaluation of electronic communications that could represent social engineering attacks, code analysis to detect suspicious or malicious data, and the like.

8. The generative AI system of claim 7, wherein the LLM output is compared against a predetermined baseline of network activity, communications, code, and the like and this information is used to alert, quarantine, mitigate, contain or counterattack any detected threats or suspicious activity.

9. The generative AI system of claim 1, wherein the analytics engine includes a visual analytics module.

10. The generative AI system of claim 1, wherein the analytics engine includes a time series module.

11. The generative AI system of claim 1, further including a reinforcement learning from human feedback (RLHF) component to further refine at least one of: selection of the hypothesis object, training the machine learning model, interrogating the context-aware AI database, interrogating the probationary database, and interrogating the plurality of external sources.

12. The generative AI system of claim 1, wherein the system performs instantaneous analysis of publicly or privately available cybersecurity information which can include wired and wireless network activity analysis, evaluation of electronic communications, code analysis to detect suspicious or malicious data, and the like.

13. The generative AI system of claim 12, wherein the system output is compared against a predetermined baseline of network activity, communications, code, and the like and this information is used to alert, quarantine, mitigate, contain or counterattack any detected threats or suspicious activity.

14. The generative AI system of claim 1, wherein the data sources include an AI agent.

15. The generative AI system of claim 14, wherein at least one of the leading indicator, a threat assessment, a mitigating action associated with the corporate entity being targeted, and a bad actor has been identified, and the identity is further provided to the AI agent.

16. The generative AI system of claim 15, wherein the corporate entity or institution is at least one of anonymized and obfuscated in a way that enables insightful and actionable data to be published for other public or private corporate entities or institutions to take action on as a protective or preventative measure without revealing proprietary or confidential information about the entity being targeted.

17. The generative AI system of claim 15, wherein the AI agent is positioned locally in a regional or distributed environment with at least one of an endpoint device, and the AI agent is configured to send alerts to other communicatively coupled entities, and to execute commands, take actions.

18. The generative AI system of claim 17, wherein the AI agent performs activities that are stored as additional data, additional models, additional features, or functions within the context aware AI database (CAAD) or within the hypothesis generating and testing system (HGTS).